Not only do we overestimate how easy it is to replace humans, replacing them is often neither desirable nor the best use of AI. A better way to think about the future of AI is interlacing its strengths with those of humans. Autonomous vehicles are often posed as reducing human interaction with vehicles to a minimum. While they will take more of the cognitive load of driving off humans, in many cases it is more useful to think of a human-machine collaboration.

1. Human-Machine
Interlace
Kevin McCullagh
IxDA London
25 July 2018

2. Product strategy

3. We tend to
overestimate
technology and
underestimate
people
1

4. We tend to
overestimate
technology and
underestimate
people
Most jobs are
best tackled with
a mix of machine
and human
strengths
1 2

5. We tend to
overestimate
technology and
underestimate
people
Most jobs are
best tackled with
a mix of machine
and human
strengths
Autonomous
Vehicles will
present a myriad
of new UX
challenges
1 2 3

6. ‘There certainly will be job
disruption. Because what’s
going to happen is robots
will be able to do everything
better than us. ... I mean
all of us’.
Elon Musk, National Governors Association, 16 July 2017

8. 98%

9. Bank tellers vs. ATM machines
Full time-equivalent bank tellers
and installed ATM machines in the US
Tellers/ATMs(1000s)
500
400
300
200
100
0
1970 1980 1990 2000 2010
Source: James Bessen, How computer automation affects occupations: Technology, jobs, and skills’, 22 September 2016, Vox
ATMs

10. Bank tellers vs. ATM machines
Full time-equivalent bank tellers
and installed ATM machines in the US
Tellers/ATMs(1000s)
500
400
300
200
100
0
1970 1980 1990 2000 2010
Fulltime
equivalent
workers
ATMs
Source: James Bessen, How computer automation affects occupations: Technology, jobs, and skills’, 22 September 2016, Vox

12. Less than 8%
of Toyota’s
production line
is automated

13. ‘Machines are good
for repetitive things,
but they can’t
improve their own
efficiency or the
quality of their work.
Only people can.’
President of Toyota Manufacturing Plant,
Kentucky

14. Automation
– is expensive
– is highly inflexible
– creates quality
problems
Gorlech and Wessel

16. New technology generally
reshapes jobs, rather than
replaces them.

17. New technology generally
reshapes jobs, rather than
replaces them. It takes
on the mundane tasks,

18. New technology generally
reshapes jobs, rather than
replaces them. It takes
on the mundane tasks,
as humans tend to move
onto more complex work.

19. Most jobs are best
tackled with a mix
of machine and
human strengths
2

22. The Singularity
‘By 2029,
computers
will have
human-level
intelligence.’
Raymond Kurzweil, SXSW interview 2017

23. Narrow
Artificial
Intelligence
General
Artificial
Intelligence

24. Narrow
Artificial
Intelligence
General
Artificial
Intelligence

25. Moravec’s paradox
Hard easy

26. Moravec’s paradox
Easy hard

27. ‘We can
know more
than we
can tell...’
Michael Polanyi, 1966

28. Human
intelligence
Artificial
intelligence≠

29. [the human mind is]
‘a machine for
jumping to
conclusions’.
Daniel Kahneman, ‘Thinking, Fast and Slow’, 2012

30. [I aim to make]
‘machines
slightly more
intelligent —
or slightly
less dumb.’
John Giannandrea, Head of AI, Apple

31. ‘The real danger ... is not
machines that are more
intelligent than we are ...
The real danger is basically
clueless machines being
ceded authority far beyond
their competence.’
Daniel Dennett, ‘The Singularity—an
Urban Legend’, Edge

32. J. C. R. Licklider

33. ‘[people] will set the goals,
formulate the hypotheses,
determine the criteria, and
perform the evaluations.

34. ‘Men will set the goals, formulate
the hypotheses, determine
the criteria, and perform the
evaluations.
‘Computing machines will do
the routinizable work that must
be done to prepare the way for
insights and decisions. . .

35. ‘The symbiotic partnership will
perform intellectual operations
much more effectively than man
alone can perform them…’
J. C. R. Licklider, ‘Man-computer symbiosis,’ 1960

36. Human
strengths
Computer
strengths
Brains Brawn
Inspiration Repetition
Making judgements Following rules
Sense making Data recall
Empathy Analysis
Most
work is
made
up of...

37. Human
Machine
Interlace

38. 5 types of
collaboration
Assigned

39. Assigned
– Certain tasks in a human
workflow are outsourced
to a machine.
– The machine completes
the task unaided,
with varying levels
of instruction.

40. Assigned

41. 5 types of
collaboration
Assigned Supervised

42. Supervised
– Decision making
processes are automated,
but under a human eye.
– This mode requires the
machine to be aware
of and communicate
risks and unknowns
to human users.

43. Supervised

44. 5 types of
collaboration
Assigned Supervised Coexistent

45. Coexistent – We will increasingly
live and work alongside
intelligent machines,
sharing the same spaces,
but focusing on separate
task-flows.
– Machines in these
scenarios must be able to
effectively negotiate shared
space and anticipate
human intent.

46. Coexistent

47. 5 types of
collaboration
Assigned Supervised Coexistent Assistive

48. Assistive
– Machines that will help
us perform tasks faster
and better.
– They support particular
tasks in human workflows,
and will excel in
discerning human goals
and learning their
preferences.

49. Source: Jaguar Land Rover Bike Sense. Seat shoulder taps the rings a bicycle bell if it senses a cyclist near the car
and Door handles ‘buzz’ to prevent doors being opened into the path of bikes
Assistive

50. 5 types of
collaboration
Assigned Supervised Coexistent Assistive Symbiotic

51. Symbiotic
– This emerging mode of
collaboration is a highly
interactive and reciprocal.
– People input strategic
hypotheses and the
machine suggests tactical
options.

52. Autonomous Vehicles
will present a myriad
of new UX challenges
3

53. 2000 2010 2020 2030 2040
Servitisation
Mobility as a service
1 4
Automation
Autonomous vehicles
Electrification
Electric vehicles
2
Zonification
Urban zoning
3
AVs are just one moving
part in the future
of mobility

54. Servitisation

55. Electrification

56. Zonification
Dynamic
kerbspace

57. Automation

58. Most ask when
Predictions and targets of AV penetration across global markets
2015 2020 2025 2030 2035 2040 2045 2050
Canalys
McKinsey
UoTA
IoEEE Amica Research
Thatcham
Thatcham
Oliver Wyman
BCG
IMechE100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
Tony Seba
KPMG
Fehr Peers
Share of car sales
Share of cars on the roads

59. Headline 30ptWhere?

60. Headline 30ptWhere?

61. Headline 30ptWhere?

62. Headline 30ptNot here!

63. The tech is not ready
Scenarios that GM’s self-driving cars have trouble handling
or aren’t being tested broadly in San Francisco
Changing lands
Tunnels
U-turns
Construction zones
Orange cones on road
Pull-over at curb
Narrow two-way street
Distinguishing between
motocycles and bikes
Going around cars trying
to parallel park
Unprotected left turns
Crossing solid white lines
Go through steam
emanating from manholes
‘Zipper merge’ (when two
lanes merge into one)
Intersections with faint
traffic lights
‘Soft’ poles that
separate lanes
Passing a cyclist
Bushes that protrude
into lanes
Heavy rain
Low sun
Going around cross-traffic
that’s stuck in intersection
Source: The Information reporting

64. Headline 30ptWho?

65. Headline 30ptWho?

66. Headline 30ptWho?

67. Headline 30ptHow?

68. Headline 30ptHow?

69. What?

70. What?

71. What?

72. Driving
Tacit
motor
skills
Collaborating
Conscious
decision
making

73. Headline 30pt

74. Headline 30pt
Sociology before
technology

75. Champion
human strengths
in an age of
automation

76. We join the dots
www.plan.london
@kevinmccull
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